This week didn’t revolve around a single theme. It wasn’t a neat follow-up to last week’s “product fundamentals,” nor a deep dive into “triple-speed coding” like 10.19. It felt more like a handful of scattered observations and experiments, each sparking new thoughts. If there’s a shared thread, it’s probably this: in an era where AI rewires productivity, what actually matters?
When Productivity Explodes, Taste Becomes Scarce
I spent a day at Google’s San Francisco office for an event. The content stayed fairly high-level, but the signal was solid. The speakers highlighted three shifts in AI-era products that stuck with me: first, intent becomes the input—users increasingly describe what they want instead of manually steering the workflow. Second, multimodality—models now handle unstructured data with astonishing ease. Third, automation—products deliver completed outcomes and keep working even when you’re not touching them.
Line those up and it’s clear many product forms are due for an overhaul. The third point—automation—feels especially worth digging into. ChatGPT only works when you prompt it, but great humans have agency; they put in unseen effort on your behalf. Future products that show that same “agency,” acting proactively instead of passively reacting, would be a qualitative leap.
An audience member raised a question that stuck with me. She’s a parent and a computer-science grad who loves the field and has enjoyed the AI tailwinds, but she worries her child won’t have the same path. David Benjamin, co-founder of Google’s AI Future Fund, said it’s a tough one, yet his instinct is:
We’re living in the most productive moment in human history. You can speak an app into existence, create an illustration or even a video. That makes taste extremely important—and taste is cultivated through the humanities, through history and art.
That resonates. When AI lowers the barrier to technical execution, differentiation shifts from can you build it to is what you built any good. Deciding what “good” looks like takes taste. And taste isn’t forged by grinding LeetCode or memorizing design patterns; it draws on a broad humanities foundation.
The Efficiency Edge of Small Teams Has Theory Behind It
I also listened to an excellent podcast this week—“OpusClip’s Growth Playbook: The One Thing I’d Do at Each Stage.” One observation mirrored what I’ve been practicing: startups should lean hard on SaaS instead of reflexively hiring or building in-house. The guest, Juntao, keeps more than a dozen SaaS tools in active use.
The U.S. really is a SaaS candy store, and pricing is friendly. We wanted to send a newsletter to boost retention. In the past we’d queue it with an internal team, wait a couple of days, then get the send. This time I just bought a service for $35 a month. It came with a WYSIWYG editor, detailed open-rate analytics—the works. Setup took under two hours and the email went out immediately.
We also needed a lightweight search module. Previously that meant spinning up an embedding inference service, wiring ElasticSearch, and slogging through infra. This time we dropped the docs into Pinecone. The free tier covered everything; we just uploaded documents and issued queries, no maintenance required.
Even user research—which feels complicated—now has SaaS options that cover recruitment, interviews, and analysis end-to-end.
If you’re a resource-strapped startup, not using these tools often comes down to “we didn’t know.” For mid-sized companies there’s another force: inertia. When you have a bench of engineers, you feel compelled to keep them busy. The result can be a tangle of half-baked internal modules and a constant slog fixing non-core issues—exactly what I’ve run into lately. Meanwhile the truly critical work—product validation, business validation—gets starved.
So yes, there’s theory behind why small teams can outpace larger ones. Once an organization grows, keeping the efficiency edge really does take skill.
Shifting From Build to Growth
Spurred by that podcast, I spent a chunk of the week hunting for our product’s first batch of core users. I haven’t done much of that before, and it’s genuinely hard.
We tried several channels. First, funneling traffic from an older product. The conversion rate was awful. It makes sense—the personas differ—but it was a free lunch worth testing.
Then we posted on Reddit and picked up a few users. The gatekeeping is real, though. Many subreddits have strict moderation, and one of my blunt promotional posts got me permanently banned. Lesson learned: in a community you can’t just drop your product and run. You have to internalize the culture, earn trust, and then share naturally.
Reddit still revealed some gems: communities like r/alphaandbetausers and r/startup where founders swap products and feedback. The vibe is fantastic.
Right now I’m watching Twitter. Judging from view counts, distribution is faster there. But if the podcast is right, certain Reddit boards probably host denser pockets of core users, so we’ll keep doubling down.
AI makes spinning up an app easy; finding the first true users is still tough—at least for me. Juntao and the OpusClip team excel at it, and the episode is packed with actionable tactics. We’ve always been stronger on build, so now it’s time to invest in user discovery and growth.
That dovetails with last week’s notes. On 10.26 I wrote “efficiency isn’t direction.” Composio’s CEO speaks imperfect English, yet kept posting videos on Twitter until one clicked. Doing it myself drives the lesson home: building is only step one. Getting the right people to know you exist can be harder—and more important.
Connecting the dots, a faint storyline emerges. AI keeps shrinking the cost of execution. We’ve experienced 5,700 lines of code overnight and compressing three weeks of work into one. SaaS pushes infrastructure costs even lower—Mailgun for newsletters in two hours, Pinecone’s free tier for search.
But AI doesn’t lower the bar for doing the right things. What makes a great product? Who are the users? How do you reach them and keep the conversation alive? Those questions still demand taste, judgment, and a ton of practice.
Maybe that’s why small teams really can be more efficient. When implementation costs plummet, the core value of an organization shifts from “how much resource can we marshal” to “how quickly can we make the right calls.” Decision velocity tends to drop as teams grow.
See you next week.